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flxai/corona-gdp.ipynb

Last active November 1, 2021 20:06
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Correlation between corona deaths per million and GDP per capita
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corona-gdp.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "5550a5f2-9305-419b-bbe4-403452706338",
"metadata": {},
"source": [
"# Correlation between corona deaths per million and GDP per capita"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ce9948c1-ecf1-4467-acdd-8cf769a5afbd",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import regex as re\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "e617b54b-dfdd-424a-93ec-2ff73dea0421",
"metadata": {},
"source": [
"### Corona deaths per million country's inhabitants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "67ca2ff0-2c59-4a5d-9170-11ae0faa6b55",
"metadata": {},
"outputs": [],
"source": [
"# https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/\n",
"df_deaths = pd.read_excel('statistic_id1104709_covid-19-cases-and-deaths-per-million-in-206-countries-as-of-october-28-2021.xlsx', sheet_name='Data', skiprows=4)\n",
"df_deaths = df_deaths.iloc[:, 1:]\n",
"df_deaths = df_deaths.rename(columns={df_deaths.columns[0]: 'Country'})\n",
"df_deaths['Country'] = df_deaths['Country'].replace('¹', '', regex=True)\n",
"df_deaths['Country'] = df_deaths['Country'].replace('USA', 'United States', regex=True)\n",
"df_deaths_sel = df_deaths[['Country', 'Deaths per million (total)']]"
]
},
{
"cell_type": "markdown",
"id": "8b1fe4da-ec90-4ca0-b4a5-e9c82a724fde",
"metadata": {},
"source": [
"### GDP per capita"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "009b58cf-05cf-4627-b758-39fac68df2e2",
"metadata": {},
"outputs": [],
"source": [
"# ⚠ Bad data quality, countries that want to hide this information actually do\n",
"# https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD\n",
"# df_gdp = pd.read_excel('API_NY.GDP.PCAP.PP.CD_DS2_en_excel_v2_3158982.xls', sheet_name='Data', skiprows=3)\n",
"\n",
"# Use CIA's estimates instead\n",
"# https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita\n",
"# https://www.cia.gov/the-world-factbook/field/real-gdp-per-capita/country-comparison\n",
"df_gdp = pd.read_csv('export_gdp.csv')\n",
"df_gdp['value_dollar'] = df_gdp.value.apply(lambda x: re.sub(r'(\\$|,)', r'', x)).astype(int)\n",
"df_gdp = df_gdp.rename(columns={'name': 'Country', 'value_dollar': 'GDP'})\n",
"df_gdp_sel = df_gdp[['Country', 'GDP']]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4c5d280b-7989-4207-8d2d-9e85da05a5ae",
"metadata": {},
"outputs": [],
"source": [
"# sorted(df_gdp.Country.unique())"
]
},
{
"cell_type": "markdown",
"id": "d729a68c-b311-42da-9aca-524b3011e8e0",
"metadata": {},
"source": [
"### Population median age by country"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d88d7185-0c82-4927-92f2-369fa8ad8720",
"metadata": {},
"outputs": [],
"source": [
"# Population above 65 in % of total population\n",
"# ⚠ Indicator imprecise\n",
"# https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS\n",
"# df_pop = pd.read_excel('API_SP.POP.65UP.TO.ZS_DS2_en_excel_v2_3159269.xls', sheet_name='Data', skiprows=3)\n",
"# df_pop_sel = df_pop[['Country Name', '2020']].dropna().rename(columns={'Country Name': 'Country', '2020': 'Population above 65 (in % of total)'})\n",
"\n",
"# Population median age by country\n",
"# https://www.cia.gov/the-world-factbook/field/median-age/country-comparison\n",
"df_pop = pd.read_csv('export_pop.csv')\n",
"df_pop_sel = df_pop[['name', 'value']].rename(columns={'name': 'Country', 'value': 'Median age'})"
]
},
{
"cell_type": "markdown",
"id": "92818253-340b-4863-abfc-15a89e57c4ae",
"metadata": {},
"source": [
"### Avoidable mortality"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ccfec4b4-6cbf-4ad4-97ce-32821e8b6cad",
"metadata": {},
"outputs": [],
"source": [
"# Avoidable mortality\n",
"# http://stats.oecd.org/index.aspx?queryid=96018\n",
"# ⚠ XLS export cannot be read and CSV file is convoluted, therefore convert using libreoffice\n",
"# ⚠ Uncomment line below to convert downloaded XLS export\n",
"# !libreoffice --headless --convert-to xls --outdir converted 751c148d-737a-4528-aa48-15211c4ce174.xls\n",
"# ⚠ Bad data quality, only 40 countries have indicators, also disputed indicator\n",
"# df_med = pd.read_csv('HEALTH_STAT_29102021193805692.csv')\n",
"# df_med.loc[(df_med['Variable'] == 'Avoidable mortality (preventable+treatable)')].sort_values('Year').groupby('Country').last()['Value']"
]
},
{
"cell_type": "markdown",
"id": "db31a57c-088f-4559-86c1-d654c75f84b5",
"metadata": {},
"source": [
"### Connect data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "96b4ccf9-5206-4d50-a3cd-dc4424d9935a",
"metadata": {},
"outputs": [],
"source": [
"df_plt = df_deaths_sel.set_index('Country').join(df_gdp_sel.set_index('Country'))\n",
"df_plt = df_plt.join(df_pop_sel.set_index('Country'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "516bd7e3-b395-43fd-9d6e-c013d5b2c5a1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Deaths per million (total)</th>\n",
" <th>GDP</th>\n",
" <th>Median age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Laos</th>\n",
" <td>8.23</td>\n",
" <td>7800.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>5.69</td>\n",
" <td>42400.0</td>\n",
" <td>37.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>3.47</td>\n",
" <td>16400.0</td>\n",
" <td>38.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Deaths per million (total) GDP Median age\n",
"Country \n",
"Laos 8.23 7800.0 24.0\n",
"New Zealand 5.69 42400.0 37.2\n",
"China 3.47 16400.0 38.4"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Highlight outliers on the bottom right\n",
"df_plt.loc[(df_plt['GDP'] > 1.7e3) & (df_plt['Deaths per million (total)'] < 1e1)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1089c126-d8d8-4253-933b-f4141470b750",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Deaths per million (total)</th>\n",
" <th>GDP</th>\n",
" <th>Median age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Peru</th>\n",
" <td>6156.45</td>\n",
" <td>11300.0</td>\n",
" <td>29.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Deaths per million (total) GDP Median age\n",
"Country \n",
"Peru 6156.45 11300.0 29.1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Highlight outlier in the top center\n",
"df_plt.loc[df_plt['Deaths per million (total)'] > 6e3]"
]
},
{
"cell_type": "markdown",
"id": "a5756d6e-adaa-4ed0-84d5-9e71f60bfcd4",
"metadata": {},
"source": [
"### Identify outliers in log-space using PCA"
]
},
{
"cell_type": "markdown",
"id": "e4829bd3-7acb-433a-bbc7-242fc0facc5e",
"metadata": {},
"source": [
"1. Transform samples $(x, y)$ to $\\log$ space\n",
"2. Select outliers by using standard deviation\n",
"3. Fit OLS in $\\log$ space, discarding outliers\n",
"4. Make predictions in $\\log$ space\n",
"5. Transform back to original space"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ff76c7ac-f5f4-4d1b-be16-f19e08bd85a3",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1ef8925f-7b2f-400c-8cd3-09dcedeeef93",
"metadata": {},
"outputs": [],
"source": [
"# Use GDP and deaths as x and y\n",
"x, y = np.hsplit(df_plt[['GDP', 'Deaths per million (total)']].dropna().values, 2)\n",
"\n",
"# Transform to log-space\n",
"xl = np.log10(x)\n",
"yl = np.log10(y)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3dd404d9-6167-4426-855a-b7cd62d9a12c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PCA(n_components=2, svd_solver='full')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Merge both to be considered by PCA\n",
"X_pca = np.concatenate([xl, yl], axis=1)\n",
"\n",
"# Fit PCA on merged results\n",
"pca = PCA(n_components=2, svd_solver='full')\n",
"pca.fit(X_pca)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "90766103-c151-4fd8-9e0b-1a860bf8c752",
"metadata": {},
"outputs": [],
"source": [
"# Split merged transformations back\n",
"xl_pca, yl_pca = np.hsplit(pca.transform(X_pca), 2)\n",
"\n",
"# Filter using y component of PCA\n",
"idx_filt = yl_pca > yl_pca.std()"
]
},
{
"cell_type": "markdown",
"id": "e869d3f4-22bc-44af-a870-8467fc10ab88",
"metadata": {},
"source": [
"### Fit OLS in log-space"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "42c00c4e-fdbb-44c6-ae79-6029a31757fe",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b390eadd-217d-44ba-bae5-d6fdfb7cdf2e",
"metadata": {},
"outputs": [],
"source": [
"# Fit linear regressor on filtered sampled\n",
"reg = LinearRegression().fit(xl[~idx_filt, None], yl[~idx_filt, None])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "59f57e32-6bc2-413a-8dd8-7720c943c4b1",
"metadata": {},
"outputs": [],
"source": [
"# Infer predictions for whole log-space\n",
"r = int(1e3)\n",
"xi = np.linspace(xl.min(), xl.max(), r)\n",
"yi = reg.predict(xi[:, None])"
]
},
{
"cell_type": "markdown",
"id": "f0362b21-10b1-49d5-8a61-443eb3a7dbf1",
"metadata": {},
"source": [
"### Transform OLS to original space"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "00f5e75f-8364-4434-b2a8-b88ac2664948",
"metadata": {},
"outputs": [],
"source": [
"# Transform from log-space back to original\n",
"xo = 10 ** xi\n",
"yo = 10 ** yi"
]
},
{
"cell_type": "markdown",
"id": "38f19f90-c5bb-40d4-af17-fc8c647e4318",
"metadata": {},
"source": [
"### Plot data "
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3fc3c465-7574-4a03-80ae-e140af173d7a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with sns.axes_style('whitegrid'):\n",
" fig = plt.figure(figsize=(12, 12))\n",
" sns.scatterplot(x='GDP', y='Deaths per million (total)', hue='Median age', palette='turbo', data=df_plt)\n",
" plt.yscale('log')\n",
" plt.xscale('log')\n",
" plt.xlabel('GDP per capita (CIA)')\n",
" plt.title('Number of corona deaths per million and country\\'s median age and GDP per capita')\n",
" plt.tight_layout()\n",
" plt.plot(xo, yo, ':', color='grey')\n",
" plt.savefig('image.png')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "fd2d9055-3984-435b-a6cd-205dbf28b7b4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_6d514_row0_col0, #T_6d514_row1_col1, #T_6d514_row2_col2 {\n",
" background-color: #b40426;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_6d514_row0_col1, #T_6d514_row0_col2, #T_6d514_row1_col0 {\n",
" background-color: #3b4cc0;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_6d514_row1_col2 {\n",
" background-color: #ead5c9;\n",
" color: #000000;\n",
"}\n",
"#T_6d514_row2_col0 {\n",
" background-color: #82a6fb;\n",
" color: #f1f1f1;\n",
"}\n",
"#T_6d514_row2_col1 {\n",
" background-color: #f6bda2;\n",
" color: #000000;\n",
"}\n",
"</style>\n",
"<table id=\"T_6d514_\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th class=\"col_heading level0 col0\" >Deaths per million (total)</th>\n",
" <th class=\"col_heading level0 col1\" >GDP</th>\n",
" <th class=\"col_heading level0 col2\" >Median age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_6d514_level0_row0\" class=\"row_heading level0 row0\" >Deaths per million (total)</th>\n",
" <td id=\"T_6d514_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
" <td id=\"T_6d514_row0_col1\" class=\"data row0 col1\" >0.595382</td>\n",
" <td id=\"T_6d514_row0_col2\" class=\"data row0 col2\" >0.684349</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6d514_level0_row1\" class=\"row_heading level0 row1\" >GDP</th>\n",
" <td id=\"T_6d514_row1_col0\" class=\"data row1 col0\" >0.595382</td>\n",
" <td id=\"T_6d514_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
" <td id=\"T_6d514_row1_col2\" class=\"data row1 col2\" >0.858437</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6d514_level0_row2\" class=\"row_heading level0 row2\" >Median age</th>\n",
" <td id=\"T_6d514_row2_col0\" class=\"data row2 col0\" >0.684349</td>\n",
" <td id=\"T_6d514_row2_col1\" class=\"data row2 col1\" >0.858437</td>\n",
" <td id=\"T_6d514_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7fb4994804c0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_plt.corr(method='spearman').style.background_gradient(cmap='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3754e690-6c32-4c84-beb8-be0e8ff31e81",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "03f60472-3ca4-45fb-aa84-2bee300501ac",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Deaths per million (total)</th>\n",
" <th>GDP</th>\n",
" <th>Median age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>191.00</td>\n",
" <td>2000.0</td>\n",
" <td>19.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>1013.60</td>\n",
" <td>13300.0</td>\n",
" <td>34.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>137.13</td>\n",
" <td>10700.0</td>\n",
" <td>28.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angola</th>\n",
" <td>53.51</td>\n",
" <td>6200.0</td>\n",
" <td>15.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Argentina</th>\n",
" <td>2578.82</td>\n",
" <td>19700.0</td>\n",
" <td>32.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armenia</th>\n",
" <td>2079.63</td>\n",
" <td>12600.0</td>\n",
" <td>36.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Australia</th>\n",
" <td>66.87</td>\n",
" <td>48700.0</td>\n",
" <td>37.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Austria</th>\n",
" <td>1271.70</td>\n",
" <td>51900.0</td>\n",
" <td>44.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Azerbaijan</th>\n",
" <td>694.98</td>\n",
" <td>13700.0</td>\n",
" <td>32.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bahrain</th>\n",
" <td>848.78</td>\n",
" <td>40900.0</td>\n",
" <td>32.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bangladesh</th>\n",
" <td>170.76</td>\n",
" <td>4800.0</td>\n",
" <td>27.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belarus</th>\n",
" <td>482.00</td>\n",
" <td>19100.0</td>\n",
" <td>40.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belgium</th>\n",
" <td>2259.31</td>\n",
" <td>48200.0</td>\n",
" <td>41.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Benin</th>\n",
" <td>13.64</td>\n",
" <td>3300.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bolivia</th>\n",
" <td>1642.56</td>\n",
" <td>7900.0</td>\n",
" <td>25.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bosnia and Herzegovina</th>\n",
" <td>3468.65</td>\n",
" <td>14300.0</td>\n",
" <td>43.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Botswana</th>\n",
" <td>1042.67</td>\n",
" <td>16000.0</td>\n",
" <td>25.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brazil</th>\n",
" <td>2874.58</td>\n",
" <td>14100.0</td>\n",
" <td>33.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bulgaria</th>\n",
" <td>3382.28</td>\n",
" <td>22400.0</td>\n",
" <td>43.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burkina Faso</th>\n",
" <td>10.53</td>\n",
" <td>2200.0</td>\n",
" <td>17.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Burundi</th>\n",
" <td>3.30</td>\n",
" <td>700.0</td>\n",
" <td>17.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td>167.29</td>\n",
" <td>4200.0</td>\n",
" <td>26.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cameroon</th>\n",
" <td>65.16</td>\n",
" <td>3600.0</td>\n",
" <td>18.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Canada</th>\n",
" <td>769.74</td>\n",
" <td>45900.0</td>\n",
" <td>41.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Central African Republic</th>\n",
" <td>21.07</td>\n",
" <td>900.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chad</th>\n",
" <td>10.91</td>\n",
" <td>1500.0</td>\n",
" <td>16.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chile</th>\n",
" <td>1989.07</td>\n",
" <td>23300.0</td>\n",
" <td>35.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td>3.47</td>\n",
" <td>16400.0</td>\n",
" <td>38.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Colombia</th>\n",
" <td>2526.03</td>\n",
" <td>13400.0</td>\n",
" <td>31.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Costa Rica</th>\n",
" <td>1388.39</td>\n",
" <td>19700.0</td>\n",
" <td>32.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cote d'Ivoire</th>\n",
" <td>26.87</td>\n",
" <td>5200.0</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Croatia</th>\n",
" <td>2241.18</td>\n",
" <td>26500.0</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cuba</th>\n",
" <td>724.40</td>\n",
" <td>12300.0</td>\n",
" <td>42.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cyprus</th>\n",
" <td>477.23</td>\n",
" <td>37700.0</td>\n",
" <td>37.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Czechia</th>\n",
" <td>2874.21</td>\n",
" <td>38300.0</td>\n",
" <td>43.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Denmark</th>\n",
" <td>465.06</td>\n",
" <td>55900.0</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dominican Republic</th>\n",
" <td>383.46</td>\n",
" <td>17000.0</td>\n",
" <td>27.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ecuador</th>\n",
" <td>1897.01</td>\n",
" <td>10300.0</td>\n",
" <td>28.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Egypt</th>\n",
" <td>183.57</td>\n",
" <td>12000.0</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>El Salvador</th>\n",
" <td>557.21</td>\n",
" <td>8100.0</td>\n",
" <td>27.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Equatorial Guinea</th>\n",
" <td>120.21</td>\n",
" <td>17000.0</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eritrea</th>\n",
" <td>12.87</td>\n",
" <td>1600.0</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Estonia</th>\n",
" <td>1117.90</td>\n",
" <td>35600.0</td>\n",
" <td>43.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eswatini</th>\n",
" <td>1081.76</td>\n",
" <td>8400.0</td>\n",
" <td>23.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ethiopia</th>\n",
" <td>57.21</td>\n",
" <td>2300.0</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Finland</th>\n",
" <td>208.32</td>\n",
" <td>47300.0</td>\n",
" <td>42.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>France</th>\n",
" <td>1668.66</td>\n",
" <td>42000.0</td>\n",
" <td>41.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gabon</th>\n",
" <td>108.17</td>\n",
" <td>14400.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>2653.76</td>\n",
" <td>14100.0</td>\n",
" <td>38.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Germany</th>\n",
" <td>1148.63</td>\n",
" <td>50900.0</td>\n",
" <td>47.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ghana</th>\n",
" <td>38.60</td>\n",
" <td>5300.0</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Greece</th>\n",
" <td>1471.59</td>\n",
" <td>27300.0</td>\n",
" <td>45.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guatemala</th>\n",
" <td>895.08</td>\n",
" <td>8400.0</td>\n",
" <td>23.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guinea</th>\n",
" <td>30.15</td>\n",
" <td>2700.0</td>\n",
" <td>19.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guinea-Bissau</th>\n",
" <td>73.40</td>\n",
" <td>1800.0</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Haiti</th>\n",
" <td>58.78</td>\n",
" <td>2800.0</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Honduras</th>\n",
" <td>1047.60</td>\n",
" <td>5100.0</td>\n",
" <td>24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hungary</th>\n",
" <td>3136.86</td>\n",
" <td>31000.0</td>\n",
" <td>43.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>India</th>\n",
" <td>333.47</td>\n",
" <td>6100.0</td>\n",
" <td>28.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indonesia</th>\n",
" <td>529.51</td>\n",
" <td>11400.0</td>\n",
" <td>31.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iran</th>\n",
" <td>1516.22</td>\n",
" <td>12400.0</td>\n",
" <td>31.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iraq</th>\n",
" <td>586.65</td>\n",
" <td>9300.0</td>\n",
" <td>21.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ireland</th>\n",
" <td>1100.08</td>\n",
" <td>89700.0</td>\n",
" <td>37.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Israel</th>\n",
" <td>891.72</td>\n",
" <td>38300.0</td>\n",
" <td>30.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Italy</th>\n",
" <td>2188.39</td>\n",
" <td>39000.0</td>\n",
" <td>46.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jamaica</th>\n",
" <td>743.48</td>\n",
" <td>8700.0</td>\n",
" <td>29.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Japan</th>\n",
" <td>144.43</td>\n",
" <td>41400.0</td>\n",
" <td>48.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>1087.24</td>\n",
" <td>9800.0</td>\n",
" <td>23.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kazakhstan</th>\n",
" <td>919.36</td>\n",
" <td>25300.0</td>\n",
" <td>31.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kenya</th>\n",
" <td>100.16</td>\n",
" <td>4200.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kosovo</th>\n",
" <td>1658.63</td>\n",
" <td>10800.0</td>\n",
" <td>30.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kuwait</th>\n",
" <td>584.97</td>\n",
" <td>49900.0</td>\n",
" <td>29.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kyrgyzstan</th>\n",
" <td>412.12</td>\n",
" <td>4700.0</td>\n",
" <td>27.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Laos</th>\n",
" <td>8.23</td>\n",
" <td>7800.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Latvia</th>\n",
" <td>1625.38</td>\n",
" <td>29900.0</td>\n",
" <td>44.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebanon</th>\n",
" <td>1235.76</td>\n",
" <td>11600.0</td>\n",
" <td>33.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lesotho</th>\n",
" <td>309.14</td>\n",
" <td>2300.0</td>\n",
" <td>24.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Liberia</th>\n",
" <td>58.13</td>\n",
" <td>1400.0</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Libya</th>\n",
" <td>744.82</td>\n",
" <td>10300.0</td>\n",
" <td>25.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lithuania</th>\n",
" <td>2052.50</td>\n",
" <td>36700.0</td>\n",
" <td>44.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Madagascar</th>\n",
" <td>35.71</td>\n",
" <td>1500.0</td>\n",
" <td>20.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malawi</th>\n",
" <td>123.30</td>\n",
" <td>1500.0</td>\n",
" <td>16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Malaysia</th>\n",
" <td>897.47</td>\n",
" <td>26400.0</td>\n",
" <td>29.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mali</th>\n",
" <td>28.54</td>\n",
" <td>2200.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mauritania</th>\n",
" <td>175.00</td>\n",
" <td>5000.0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mauritius</th>\n",
" <td>126.41</td>\n",
" <td>19500.0</td>\n",
" <td>36.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mexico</th>\n",
" <td>2248.77</td>\n",
" <td>17900.0</td>\n",
" <td>29.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Moldova</th>\n",
" <td>2866.83</td>\n",
" <td>12300.0</td>\n",
" <td>37.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mongolia</th>\n",
" <td>526.79</td>\n",
" <td>11500.0</td>\n",
" <td>29.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Morocco</th>\n",
" <td>401.60</td>\n",
" <td>6900.0</td>\n",
" <td>29.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mozambique</th>\n",
" <td>63.52</td>\n",
" <td>1200.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Namibia</th>\n",
" <td>1423.11</td>\n",
" <td>8900.0</td>\n",
" <td>21.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nepal</th>\n",
" <td>397.78</td>\n",
" <td>3800.0</td>\n",
" <td>25.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Netherlands</th>\n",
" <td>1033.82</td>\n",
" <td>54200.0</td>\n",
" <td>42.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Zealand</th>\n",
" <td>5.69</td>\n",
" <td>42400.0</td>\n",
" <td>37.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nicaragua</th>\n",
" <td>31.78</td>\n",
" <td>5300.0</td>\n",
" <td>27.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Niger</th>\n",
" <td>9.05</td>\n",
" <td>1200.0</td>\n",
" <td>14.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nigeria</th>\n",
" <td>14.36</td>\n",
" <td>4900.0</td>\n",
" <td>18.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Norway</th>\n",
" <td>168.29</td>\n",
" <td>63600.0</td>\n",
" <td>39.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oman</th>\n",
" <td>826.33</td>\n",
" <td>27300.0</td>\n",
" <td>26.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pakistan</th>\n",
" <td>131.20</td>\n",
" <td>4600.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Panama</th>\n",
" <td>1722.38</td>\n",
" <td>25400.0</td>\n",
" <td>30.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Papua New Guinea</th>\n",
" <td>38.17</td>\n",
" <td>4100.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Paraguay</th>\n",
" <td>2305.02</td>\n",
" <td>12300.0</td>\n",
" <td>29.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Peru</th>\n",
" <td>6156.45</td>\n",
" <td>11300.0</td>\n",
" <td>29.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Philippines</th>\n",
" <td>391.69</td>\n",
" <td>8000.0</td>\n",
" <td>24.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Poland</th>\n",
" <td>2019.23</td>\n",
" <td>32200.0</td>\n",
" <td>41.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Portugal</th>\n",
" <td>1766.80</td>\n",
" <td>32200.0</td>\n",
" <td>44.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Puerto Rico</th>\n",
" <td>1010.74</td>\n",
" <td>33400.0</td>\n",
" <td>43.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qatar</th>\n",
" <td>215.04</td>\n",
" <td>85300.0</td>\n",
" <td>33.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Romania</th>\n",
" <td>2377.23</td>\n",
" <td>28800.0</td>\n",
" <td>42.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Russia</th>\n",
" <td>1590.82</td>\n",
" <td>26500.0</td>\n",
" <td>40.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rwanda</th>\n",
" <td>104.70</td>\n",
" <td>2100.0</td>\n",
" <td>19.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Saudi Arabia</th>\n",
" <td>256.36</td>\n",
" <td>44300.0</td>\n",
" <td>30.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Senegal</th>\n",
" <td>115.24</td>\n",
" <td>3300.0</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Serbia</th>\n",
" <td>1396.41</td>\n",
" <td>18200.0</td>\n",
" <td>43.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sierra Leone</th>\n",
" <td>15.49</td>\n",
" <td>1600.0</td>\n",
" <td>19.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Singapore</th>\n",
" <td>61.19</td>\n",
" <td>93400.0</td>\n",
" <td>35.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovakia</th>\n",
" <td>2375.66</td>\n",
" <td>30300.0</td>\n",
" <td>41.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Slovenia</th>\n",
" <td>2257.24</td>\n",
" <td>36500.0</td>\n",
" <td>44.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Somalia</th>\n",
" <td>78.22</td>\n",
" <td>800.0</td>\n",
" <td>18.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Africa</th>\n",
" <td>1520.69</td>\n",
" <td>11500.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Sudan</th>\n",
" <td>12.02</td>\n",
" <td>1600.0</td>\n",
" <td>18.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spain</th>\n",
" <td>1854.18</td>\n",
" <td>36200.0</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sri Lanka</th>\n",
" <td>627.16</td>\n",
" <td>12500.0</td>\n",
" <td>33.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sudan</th>\n",
" <td>72.38</td>\n",
" <td>4000.0</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sweden</th>\n",
" <td>1459.93</td>\n",
" <td>50700.0</td>\n",
" <td>41.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Switzerland</th>\n",
" <td>1306.96</td>\n",
" <td>68400.0</td>\n",
" <td>42.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Syria</th>\n",
" <td>148.45</td>\n",
" <td>2900.0</td>\n",
" <td>23.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tajikistan</th>\n",
" <td>13.41</td>\n",
" <td>3700.0</td>\n",
" <td>25.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tanzania</th>\n",
" <td>12.50</td>\n",
" <td>2600.0</td>\n",
" <td>18.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td>271.77</td>\n",
" <td>17300.0</td>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Timor-Leste</th>\n",
" <td>93.57</td>\n",
" <td>3200.0</td>\n",
" <td>19.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Togo</th>\n",
" <td>29.94</td>\n",
" <td>2100.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trinidad and Tobago</th>\n",
" <td>1188.55</td>\n",
" <td>23700.0</td>\n",
" <td>37.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tunisia</th>\n",
" <td>2156.61</td>\n",
" <td>9700.0</td>\n",
" <td>32.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Turkey</th>\n",
" <td>836.26</td>\n",
" <td>28400.0</td>\n",
" <td>32.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uganda</th>\n",
" <td>72.37</td>\n",
" <td>2200.0</td>\n",
" <td>15.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ukraine</th>\n",
" <td>1573.81</td>\n",
" <td>12400.0</td>\n",
" <td>41.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Arab Emirates</th>\n",
" <td>218.51</td>\n",
" <td>67100.0</td>\n",
" <td>38.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United Kingdom</th>\n",
" <td>2089.04</td>\n",
" <td>41600.0</td>\n",
" <td>40.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United States</th>\n",
" <td>2247.41</td>\n",
" <td>60200.0</td>\n",
" <td>38.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uruguay</th>\n",
" <td>1755.19</td>\n",
" <td>21600.0</td>\n",
" <td>35.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uzbekistan</th>\n",
" <td>39.07</td>\n",
" <td>7000.0</td>\n",
" <td>30.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Venezuela</th>\n",
" <td>169.59</td>\n",
" <td>7704.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vietnam</th>\n",
" <td>226.58</td>\n",
" <td>8200.0</td>\n",
" <td>31.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yemen</th>\n",
" <td>64.06</td>\n",
" <td>2500.0</td>\n",
" <td>19.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zambia</th>\n",
" <td>204.92</td>\n",
" <td>3300.0</td>\n",
" <td>16.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zimbabwe</th>\n",
" <td>319.21</td>\n",
" <td>2700.0</td>\n",
" <td>20.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Deaths per million (total) GDP Median age\n",
"Country \n",
"Afghanistan 191.00 2000.0 19.5\n",
"Albania 1013.60 13300.0 34.3\n",
"Algeria 137.13 10700.0 28.9\n",
"Angola 53.51 6200.0 15.9\n",
"Argentina 2578.82 19700.0 32.4\n",
"Armenia 2079.63 12600.0 36.6\n",
"Australia 66.87 48700.0 37.5\n",
"Austria 1271.70 51900.0 44.5\n",
"Azerbaijan 694.98 13700.0 32.6\n",
"Bahrain 848.78 40900.0 32.9\n",
"Bangladesh 170.76 4800.0 27.9\n",
"Belarus 482.00 19100.0 40.9\n",
"Belgium 2259.31 48200.0 41.6\n",
"Benin 13.64 3300.0 17.0\n",
"Bolivia 1642.56 7900.0 25.3\n",
"Bosnia and Herzegovina 3468.65 14300.0 43.3\n",
"Botswana 1042.67 16000.0 25.7\n",
"Brazil 2874.58 14100.0 33.2\n",
"Bulgaria 3382.28 22400.0 43.7\n",
"Burkina Faso 10.53 2200.0 17.9\n",
"Burundi 3.30 700.0 17.7\n",
"Cambodia 167.29 4200.0 26.4\n",
"Cameroon 65.16 3600.0 18.5\n",
"Canada 769.74 45900.0 41.8\n",
"Central African Republic 21.07 900.0 20.0\n",
"Chad 10.91 1500.0 16.1\n",
"Chile 1989.07 23300.0 35.5\n",
"China 3.47 16400.0 38.4\n",
"Colombia 2526.03 13400.0 31.2\n",
"Costa Rica 1388.39 19700.0 32.6\n",
"Cote d'Ivoire 26.87 5200.0 20.3\n",
"Croatia 2241.18 26500.0 43.9\n",
"Cuba 724.40 12300.0 42.1\n",
"Cyprus 477.23 37700.0 37.9\n",
"Czechia 2874.21 38300.0 43.3\n",
"Denmark 465.06 55900.0 42.0\n",
"Dominican Republic 383.46 17000.0 27.9\n",
"Ecuador 1897.01 10300.0 28.8\n",
"Egypt 183.57 12000.0 24.1\n",
"El Salvador 557.21 8100.0 27.7\n",
"Equatorial Guinea 120.21 17000.0 20.3\n",
"Eritrea 12.87 1600.0 20.3\n",
"Estonia 1117.90 35600.0 43.7\n",
"Eswatini 1081.76 8400.0 23.7\n",
"Ethiopia 57.21 2300.0 19.8\n",
"Finland 208.32 47300.0 42.8\n",
"France 1668.66 42000.0 41.7\n",
"Gabon 108.17 14400.0 21.0\n",
"Georgia 2653.76 14100.0 38.6\n",
"Germany 1148.63 50900.0 47.8\n",
"Ghana 38.60 5300.0 21.4\n",
"Greece 1471.59 27300.0 45.3\n",
"Guatemala 895.08 8400.0 23.2\n",
"Guinea 30.15 2700.0 19.1\n",
"Guinea-Bissau 73.40 1800.0 18.0\n",
"Haiti 58.78 2800.0 24.1\n",
"Honduras 1047.60 5100.0 24.4\n",
"Hungary 3136.86 31000.0 43.6\n",
"India 333.47 6100.0 28.7\n",
"Indonesia 529.51 11400.0 31.1\n",
"Iran 1516.22 12400.0 31.7\n",
"Iraq 586.65 9300.0 21.2\n",
"Ireland 1100.08 89700.0 37.8\n",
"Israel 891.72 38300.0 30.4\n",
"Italy 2188.39 39000.0 46.5\n",
"Jamaica 743.48 8700.0 29.4\n",
"Japan 144.43 41400.0 48.6\n",
"Jordan 1087.24 9800.0 23.5\n",
"Kazakhstan 919.36 25300.0 31.6\n",
"Kenya 100.16 4200.0 20.0\n",
"Kosovo 1658.63 10800.0 30.5\n",
"Kuwait 584.97 49900.0 29.7\n",
"Kyrgyzstan 412.12 4700.0 27.3\n",
"Laos 8.23 7800.0 24.0\n",
"Latvia 1625.38 29900.0 44.4\n",
"Lebanon 1235.76 11600.0 33.7\n",
"Lesotho 309.14 2300.0 24.7\n",
"Liberia 58.13 1400.0 18.0\n",
"Libya 744.82 10300.0 25.8\n",
"Lithuania 2052.50 36700.0 44.5\n",
"Madagascar 35.71 1500.0 20.3\n",
"Malawi 123.30 1500.0 16.8\n",
"Malaysia 897.47 26400.0 29.2\n",
"Mali 28.54 2200.0 16.0\n",
"Mauritania 175.00 5000.0 21.0\n",
"Mauritius 126.41 19500.0 36.3\n",
"Mexico 2248.77 17900.0 29.3\n",
"Moldova 2866.83 12300.0 37.7\n",
"Mongolia 526.79 11500.0 29.8\n",
"Morocco 401.60 6900.0 29.1\n",
"Mozambique 63.52 1200.0 17.0\n",
"Namibia 1423.11 8900.0 21.8\n",
"Nepal 397.78 3800.0 25.3\n",
"Netherlands 1033.82 54200.0 42.8\n",
"New Zealand 5.69 42400.0 37.2\n",
"Nicaragua 31.78 5300.0 27.3\n",
"Niger 9.05 1200.0 14.8\n",
"Nigeria 14.36 4900.0 18.6\n",
"Norway 168.29 63600.0 39.5\n",
"Oman 826.33 27300.0 26.2\n",
"Pakistan 131.20 4600.0 22.0\n",
"Panama 1722.38 25400.0 30.1\n",
"Papua New Guinea 38.17 4100.0 24.0\n",
"Paraguay 2305.02 12300.0 29.7\n",
"Peru 6156.45 11300.0 29.1\n",
"Philippines 391.69 8000.0 24.1\n",
"Poland 2019.23 32200.0 41.9\n",
"Portugal 1766.80 32200.0 44.6\n",
"Puerto Rico 1010.74 33400.0 43.6\n",
"Qatar 215.04 85300.0 33.7\n",
"Romania 2377.23 28800.0 42.5\n",
"Russia 1590.82 26500.0 40.3\n",
"Rwanda 104.70 2100.0 19.7\n",
"Saudi Arabia 256.36 44300.0 30.8\n",
"Senegal 115.24 3300.0 19.4\n",
"Serbia 1396.41 18200.0 43.4\n",
"Sierra Leone 15.49 1600.0 19.1\n",
"Singapore 61.19 93400.0 35.6\n",
"Slovakia 2375.66 30300.0 41.8\n",
"Slovenia 2257.24 36500.0 44.9\n",
"Somalia 78.22 800.0 18.5\n",
"South Africa 1520.69 11500.0 28.0\n",
"South Sudan 12.02 1600.0 18.6\n",
"Spain 1854.18 36200.0 43.9\n",
"Sri Lanka 627.16 12500.0 33.7\n",
"Sudan 72.38 4000.0 18.3\n",
"Sweden 1459.93 50700.0 41.1\n",
"Switzerland 1306.96 68400.0 42.7\n",
"Syria 148.45 2900.0 23.5\n",
"Tajikistan 13.41 3700.0 25.3\n",
"Tanzania 12.50 2600.0 18.2\n",
"Thailand 271.77 17300.0 39.0\n",
"Timor-Leste 93.57 3200.0 19.6\n",
"Togo 29.94 2100.0 20.0\n",
"Trinidad and Tobago 1188.55 23700.0 37.8\n",
"Tunisia 2156.61 9700.0 32.7\n",
"Turkey 836.26 28400.0 32.2\n",
"Uganda 72.37 2200.0 15.7\n",
"Ukraine 1573.81 12400.0 41.2\n",
"United Arab Emirates 218.51 67100.0 38.4\n",
"United Kingdom 2089.04 41600.0 40.6\n",
"United States 2247.41 60200.0 38.5\n",
"Uruguay 1755.19 21600.0 35.5\n",
"Uzbekistan 39.07 7000.0 30.1\n",
"Venezuela 169.59 7704.0 30.0\n",
"Vietnam 226.58 8200.0 31.9\n",
"Yemen 64.06 2500.0 19.8\n",
"Zambia 204.92 3300.0 16.9\n",
"Zimbabwe 319.21 2700.0 20.5"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
" display(df_plt.sort_index().dropna())"
]
},
{
"cell_type": "markdown",
"id": "7ce3415e-1f93-4d87-8b2b-79d59b59f53f",
"metadata": {},
"source": [
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
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},
"nbformat": 4,
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